Consistency analysis and improvement for single-camera localization

Hesch, Joel A.; Roumeliotis, Stergios I.

doi:10.1109/cvprw.2012.6239190

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…To avoid double counting information, care must be taken that feature measurements are used to either formulate a constraint in the local SLAM or to the global 3D map, but not both. Given the information content of the global map, measurements to the global 3D map are preferred; where additional constraints on the updates can ensure estimator consistency (Hesch and Roumeliotis, 2012).…”

Section: Global Updates To the Local State Estimationmentioning

confidence: 99%

Large-scale, real-time visual–inertial localization revisited

Lynen

Zeisl

Aiger

et al. 2020

The International Journal of Robotics Research

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The overarching goals in image-based localization are scale, robustness, and speed. In recent years, approaches based on local features and sparse 3D point-cloud models have both dominated the benchmarks and seen successful real-world deployment. They enable applications ranging from robot navigation, autonomous driving, virtual and augmented reality to device geo-localization. Recently, end-to-end learned localization approaches have been proposed which show promising results on small-scale datasets. However, the positioning accuracy, scalability, latency, and compute and storage requirements of these approaches remain open challenges. We aim to deploy localization at a global scale where one thus relies on methods using local features and sparse 3D models. Our approach spans from offline model building to real-time client-side pose fusion. The system compresses the appearance and geometry of the scene for efficient model storage and lookup leading to scalability beyond what has been demonstrated previously. It allows for low-latency localization queries and efficient fusion to be run in real-time on mobile platforms by combining server-side localization with real-time visual–inertial-based camera pose tracking. In order to further improve efficiency, we leverage a combination of priors, nearest-neighbor search, geometric match culling, and a cascaded pose candidate refinement step. This combination outperforms previous approaches when working with large-scale models and allows deployment at unprecedented scale. We demonstrate the effectiveness of our approach on a proof-of-concept system localizing 2.5 million images against models from four cities in different regions of the world achieving query latencies in the 200 ms range.

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Section: Global Updates To the Local State Estimationmentioning

confidence: 99%

Large-scale, real-time visual–inertial localization revisited

Lynen

Zeisl

Aiger

et al. 2020

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…Generally, the VIO problem has four unobservable directions, but since the linearization errors, the system only have three unobservable directions, which renders the filter inconsistent. So papers [8], [9], [11]- [13] were proposed a series of methods, e.g. first-estimates Jacobian and constraint of system observability, to improve the consistency of the system.…”

Section: Related Workmentioning

confidence: 99%

“…Filtering based approaches require fewer computational resources due to the continuous marginalization of past state, however the system get slightly lower accuracy due to the linearization error. According to the way processing the measurement information, the recursive filtering approaches can be classified into two main categories: extended kalman filter (EKF) based methods [5]- [9] and sliding window filtering approaches [10]- [13]. The state vector of EKF-based SLAM algorithms include both the pose of the platform and a set of feature positions, so as long as these features are continuously observed and contained in the state vector, the estimated pose relative to these features will not drift.…”

Section: Related Workmentioning

confidence: 99%

Accurate Monocular Visual-Inertial SLAM Using a Map-Assisted EKF Approach

Quan

Piao

Tan³

et al. 2019

IEEE Access

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This paper presents a novel tightly-coupled monocular visual-inertial Simultaneous Localization and Mapping algorithm, which provides accurate and robust localization within the globally consistent map in real time on a standard CPU. This is achieved by firstly performing the visual-inertial extended kalman filter(EKF) to provide motion estimate at a high rate.However the filter becomes inconsistent due to the well known linearization issues. So we perform a keyframe-based visual-inertial bundle adjustment to improve the consistency and accuracy of the system. In addition, a loop closure detection and correction module is also added to eliminate the accumulated drift when revisiting an area. Finally, the optimized motion estimates and map are fed back to the EKF-based visual-inertial odometry module, thus the inconsistency and estimation error of the EKF estimator are reduced. In this way, the system can continuously provide reliable motion estimates for the long-term operation. The performance of the algorithm is validated on public datasets and real-world experiments, which proves the superiority of the proposed algorithm.

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“…To solve this problem, the first-estimates Jacobian approach was proposed in [3], which computes Jacobian with the first-ever available estimate instead of different linearization points to ensure the correct observability of the system and thereby improve the consistency and accuracy of the system. In addition, the observability-constrained EKF [4] was proposed to explicitly enforce the unobservable directions of the system, hence improving the consistency and accuracy of the system.…”

Section: Related Workmentioning

confidence: 99%

Tightly-Coupled Monocular Visual-Odometric SLAM Using Wheels and a MEMS Gyroscope

Quan

Piao

Tan³

et al. 2019

IEEE Access

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In this paper, we present a novel tightly-coupled probabilistic monocular visual-odometric Simultaneous Localization and Mapping algorithm using wheels and a MEMS gyroscope, which can provide accurate, robust and long-term localization for the ground robot moving on a plane. Firstly, we present an odometer preintegration theory that integrates the wheel encoder measurements and gyroscope measurements to a local frame. The preintegration theory properly addresses the manifold structure of the rotation group SO(3) and carefully deals with uncertainty propagation and bias correction. Then the novel odometer error term is formulated using the odometer preintegration model and it is tightly integrated into the visual optimization framework. Furthermore, we introduce a complete tracking framework to provide different strategies for motion tracking when (1) both measurements are available, (2) visual measurements are not available, and (3) wheel encoder experiences slippage, which leads the system to be accurate and robust. Finally, the proposed algorithm is evaluated by performing extensive experiments, the experimental results demonstrate the superiority of the proposed system.

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Consistency analysis and improvement for single-camera localization

Cited by 11 publications

References 16 publications

Large-scale, real-time visual–inertial localization revisited

Large-scale, real-time visual–inertial localization revisited

Accurate Monocular Visual-Inertial SLAM Using a Map-Assisted EKF Approach

Tightly-Coupled Monocular Visual-Odometric SLAM Using Wheels and a MEMS Gyroscope

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